Surface roughness affects bone formation around orthopaedic implants in vivo and osteoblast functions in vitro. Osteoblast-like MG63 cells cultured on rough surfaces exhibited decreased cell number, increased differentiation and increased local factor production when compared to cells grow on smooth surfaces. In these experiments, roughness was characterized as average peak to valley height (Ra) which is not equal throughout the surface. Other features of roughness, including peak and valley area distributions and curvature of the valleys, will affect cell functions. In this study, novel titanium surfaces were prepared by photolithography to produce well designed microstructure and nanostructure. Smooth disks were made by producing craters of 10 micrometer, 30 micrometer and 100 micrometer diameters on titanium disks with constant curvatures. Craters were placed sparsely (10/1, 30/1, 100/1) or compactly (10/6, 30/6, 100/6). Smooth disks were also acid etched to make an overall roughness of Ra 0.7 micrometer or anodized to produce volcano-like nanostructure of Ra 0.4 micrometer. The results revealed the distinguishing contributions of microcrater size, crater spacing and nanostructures to surface effect on cell number, differentiation (alkaline phosphatase; osteocalcin) and local factor levels (TGF-beta1; PGE2). Cell attachment depends on crater spacing; cell growth and aggregation depend on crater dimension and cell morphology depends on the presence of nanostructural features. Cell differentiation and local factor production are modulated by acid etched roughness in concert with microstructure, and active TGF-beta1 level depends on nanoscale roughness.

La première partie de mon thèse s'intéresse aux problèmatiques de la technologie du semi-conducteur traditionnelle. Ensuite dans la deuxième partie je vais m'intérésser aux propriétés des nanocomposants. Ils se distinguent du CMOS classique selon plusieurs critères. Ayant une connaissance globale des architectures, j'ai choisi de développer plus amplement les réseaux de neurones en seconde partie. En effet, des fonctions logiques peuvent être émulées par les réseaux de neurones réalisés à partir des nanotubes de carbones et des mémoires multiniveaux. Pour la dernière partie, la robustesse d'une architecture de réseaux de neurones est évaluée par simulation qui montre la possibilité de construire un circuit robuste grâce à l'apprentissage.

Ces dernières années ont été marquées par de grandes évolutions technologiques à travers notamment une course à la miniaturisation. De gros efforts de recherche se concentrent en particulier sur le domaine de l’électronique organique mais aussi sur de nouveaux matériaux bidimensionnels comme le graphène. Ces matériaux 2D présentent des propriétés physiques exceptionnelles et sont des candidats prometteurs pour le développement de futurs dispositifs électroniques. Au cours de cette thèse, l’approche ascendante, qui consiste à assembler ensemble des petites briques élémentaires, a été utilisée pour élaborer des nanostructures bidimensionnelles originales sur des surfaces. Des états électroniques localisés dus à un couplage électronique latéral particulier entre les molécules ont été observés. Quatre nanoarchitectures hybrides ioniques-organiques différentes ont été réalisées en faisant varier la température de la surface. Des nanostructures organiques covalentes ont aussi été élaborées par une réaction de couplage d’Ullmann sur la surface. Deux précurseurs différents en forme d’étoile avec des substituants iodés et bromés respectivement, ont été étudiés. De grandes nanostructures carbonées hexagonales poreuses ont notamment été synthétisées en faisant varier la température du substrat. Ces travaux ouvrent de nouvelles perspectives pour la réalisation de matériaux organiques bidimensionnels aux propriétés contrôlées. / Over the last few years, important technological developments were made following a trend towards miniaturization. In particular, lots of research efforts are put into the research on organic electronics and on 2D materials like graphene. Such 2D materials show great physical properties and are promising candidates for the development of future electronic devices.In this project, bottom-up approach consisting in assembling elementary building blocks together, was used to engineer novel twodimensional nanostructures on metal surfaces. The properties of these two-dimensional nanostructures were investigated using Scanning Tunneling Microscopy (STM) and X-ray Photoemission Spectroscopy (XPS). Two-dimensional nanostructures based on the self-assembly of organic building blocks stabilized by intermolecular interactions were engineered. In particular, nanostructures stabilized by hydrogen bonds, halogen bonds and ionic-organic interactions were investigated. Localized electronic states due to specific molecular lateral electronic coupling were observed. Four different ionic-organic nanoarchitectures were engineered varying the substrate temperature. Covalent organic nanostructures were also engineered by onsurface Ullmann coupling reaction. Two different star-shaped precursors with iodine and bromine substituents respectively, were investigated. Large periodic porous 2D covalent hexagonal carbon nanostructures weresuccessfully engineered by temperature driven hierarchal Ullmann coupling. These results open new perspectives for the development of 2D organic materials with controlled structures and properties.

With the transition from single-core to multi-core computing and CMOS technology reaching its physical limits, new computing architectures which are scalable, robust, and low-power are required. A promising alternative to conventional computing architectures are Cellular Automata (CA) networks and Random Boolean Networks (RBN), where simple computational nodes combine to form a network that is capable of performing a larger computational task. It has previously been shown that RBNs can offer superior characteristics over mesh networks in terms of robustness, information processing capabilities, and manufacturing costs while the locally connected computing elements of a CA network provide better scalability and low average interconnect length. This study presents a low level hardware analysis of these architectures using a framework which generates the HDL code and netlist of these networks for various network parameters. The HDL code and netlists are then used to simulate these new computing architectures to estimate the latency, area and power consumed when implemented on silicon and performing a pre-determined computation. We show that for RBNs, information processing is faster compared to a CA network, but CA networks are found to a have lower and better distribution of power dissipation than RBNs because of their regular structure. A well-established task to determine the latency of operation for these architectures is presented for a good understanding of the effect of non-local connections in a network. Programming the nodes for this purpose is done externally using a novel self-configuration algorithm requiring minimal hardware. Configuration for RBNs is done by sending in configuration packets through a randomly chosen node. Logic for identifying the topology for the network is implemented for the nodes in the RBN network to enable compilers to analyze and generate the configuration bit stream for that network. On the other hand, the configuration of the CA network is done by passing in configuration data through the inputs on one of the sides of the cell array and shifting it into the network. A study of the overhead of the network configuration and topology identification mechanisms are presented. An analysis of small-world networks in terms of interconnect power and information propagation capability has been presented. It has been shown that small-world networks, whose randomness lies between that of completely regular and completely irregular networks, are realistic while providing good information propagation capability. This study provides valuable information to help designers make decisions for various performance parameters for both RBN and CA networks, and thus to find the best design for the application under consideration.